U.S. patent application number 13/640128 was filed with the patent office on 2013-02-07 for system and method for determining motion of a biological object.
This patent application is currently assigned to GE HEALTHCARE UK LIMITED. The applicant listed for this patent is Nicholas Thomas. Invention is credited to Nicholas Thomas.
Application Number | 20130034272 13/640128 |
Document ID | / |
Family ID | 42236156 |
Filed Date | 2013-02-07 |
United States Patent
Application |
20130034272 |
Kind Code |
A1 |
Thomas; Nicholas |
February 7, 2013 |
SYSTEM AND METHOD FOR DETERMINING MOTION OF A BIOLOGICAL OBJECT
Abstract
The invention provides a system and method of analysing the
motion of a biological object, particularly the motion of cultured
organisms or cell cultures. The system and method of the invention
may be used to determine the effect of a physical stimulus or a
test agent on the motion of cell cultures. The system and method is
of particular use in assessing the effect a chemical may have on
the contractile motion of cardiomyocyte cell cultures.
Inventors: |
Thomas; Nicholas; (Cardiff,
GB) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Thomas; Nicholas |
Cardiff |
|
GB |
|
|
Assignee: |
GE HEALTHCARE UK LIMITED
CHALFONT ST GILES
GB
|
Family ID: |
42236156 |
Appl. No.: |
13/640128 |
Filed: |
April 5, 2011 |
PCT Filed: |
April 5, 2011 |
PCT NO: |
PCT/EP2011/055270 |
371 Date: |
October 9, 2012 |
Current U.S.
Class: |
382/107 |
Current CPC
Class: |
G06T 2207/10016
20130101; G06T 7/254 20170101; G06T 2207/20056 20130101; G06K
9/00127 20130101; G06T 7/42 20170101; G06T 7/0016 20130101; G06T
2207/30024 20130101 |
Class at
Publication: |
382/107 |
International
Class: |
G06K 9/62 20060101
G06K009/62 |
Foreign Application Data
Date |
Code |
Application Number |
Apr 12, 2010 |
GB |
1006046.5 |
Claims
1. A system (100) for automated determination of the motion of a
biological object, the system comprising: a) an imager (104) for
acquiring a first time series of images of said object comprising
successive frames (f.sub.1 to f.sub.n) and a reference frame
(f.sub.R); b) a processor (107) that is operable to i) generate a
time series of subtractive images (f.sub.R-1 to f.sub.R-n); ii)
derive one or more measurements from said time series of
subtractive images; and iii) analyse said one or more measurements
to quantify motion in the first time series of images; and wherein
generating the time series of subtractive images (f.sub.R-1 to
f.sub.R-n) comprises subtracting each successive frame (f.sub.1 to
f.sub.n) from said reference frame (f.sub.R); or wherein generating
the time series of subtractive images (f.sub.R-1 to f.sub.R-n)
comprises subtracting the reference frame (f.sub.R) from each
successive frame (f.sub.1 to f.sub.n).
2. The system of claim 1, wherein each frame comprises a plurality
of pixels having an intensity value associated therewith and said
processor is operable to generate said time series of subtractive
images by means of said intensity values.
3. The system of claim 1, wherein said one or more measurements is
selected from the group consisting of mean pixel intensity, total
pixel intensity and median pixel intensity.
4. The system of claim 1, wherein said object is a cultured
organism or a cell culture.
5. The system of claim 4, wherein said cultured organism is
selected from the group consisting of zebra fish or C. elegans.
6. The system of claim 4, wherein said cell culture is selected
from the group consisting of bacterial cell culture, fungal cell
culture, insect cell culture, fish cell culture and mammalian cell
culture.
7. The system of claim 6, wherein said mammalian cell culture is a
human cell culture.
8. The system of claim 7, wherein said human cell culture is a
cardiomyocyte cell culture.
9. A method of determining the motion of a biological object
comprising the steps of: i) acquiring a first time series of images
of said object comprising successive frames (f1 to f.sub.n) and a
reference frame (f.sub.R); ii) generating a time series of
subtractive images (f.sub.R-1 to f.sub.R-n); iii) deriving one or
more measurements from said time series of subtractive images; and
iv) analysing said one or more measurements to quantify motion in
said first time series of images; wherein generating the time
series of subtractive images (f.sub.R-1 to f.sub.R-n) comprises
subtracting each successive frame (f.sub.1 to f.sub.n) from said
reference frame (f.sub.R); or wherein generating the time series of
subtractive images (f.sub.R-1 to f.sub.R-n) comprises subtracting
the reference frame (f.sub.R) from each successive frame (f.sub.1
to f.sub.n).
10. The method of claim 9, wherein each frame comprises a plurality
of pixels having an intensity value associated therewith and step
ii) involves either subtracting said intensity value of each said
frame from the corresponding intensity value of the reference frame
(f.sub.R) or subtracting the intensity value of the reference frame
(f.sub.R) from the corresponding intensity value of each frame.
11. The method of claim 9, wherein said one or more measurements is
selected from the group consisting of mean pixel intensity, total
pixel intensity and median pixel intensity.
12. The method of claim 9, wherein said object is a cultured
organism or a cell culture.
13. The method of claim 12, wherein said cultured organism is
selected from the group consisting of zebra fish or C. elegans
14. The method of claim 12, wherein said cell culture is selected
from the group consisting of bacterial cell culture, fungal cell
culture, insect cell culture, fish cell culture and mammalian cell
culture.
15. The method of claim 14, wherein said mammalian cell culture is
a human cell culture.
16. The method of claim 15, wherein said human cell culture is a
cardiomyocyte cell culture.
17. The method of claim 9, wherein said method is an automated
method.
18. (canceled)
19. A computer program product comprising machine instructions
operable to configure a data processing apparatus to implement the
method of claim 9.
Description
TECHNICAL FIELD
[0001] The present invention relates to the determination of motion
of a biological object, particularly to the determination of the
motion in cultured organisms or cell cultures. The invention may be
used to determine the effects of test compounds or environmental
stimuli on the motion of biological objects, such as the
contractile motion of cultures of cardiomyocytes.
BACKGROUND
[0002] The movement of cells during migration, motility, chemotaxis
or wound healing, or the movement of small organisms such as
zebrafish or nematodes is an important parameter in the study of
biological systems. For example, many studies are directed at
measuring the response of cells or cell cultures to a physical
stimulus or a chemical treatment. Toxicological studies, in
particular, often focus on the response of cell cultures to
chemical treatment to determine if a chemical has an adverse effect
on the growth and development of the test or cultured organism.
[0003] Cardiotoxicity currently accounts for 30% of drug failures
during pre-clinical and clinical development and there is a strong
demand from the pharmaceutical industry for more predictive
cellular models to reduce attrition costs. Cardiomyocytes derived
from human embryonic stem cells provide an advance towards
development of more clinically predictive assays for assessing
cardiotoxicity liabilities in new drug candidates. Cardiomyocytes
may be used in a wide range of applications including
electrophysiology, ion flux imaging and high content analysis to
assess cardiac liability of candidate drugs. Cardiomyocyte function
is controlled by an integrated system of ion channels which
modulate the influx and efflux of potassium, calcium and sodium
ions to modulate cellular contractility. Drug interference with
these control mechanisms, e.g. via interaction with the HERG
potassium channel, can lead to shortening or lengthening of
cardiomyocyte action potentials and in some cases to early or late
after-depolarisations which in-vivo may give rise to arrhythmia and
heart failure.
[0004] Measurement of cardiomyocyte beat rate is a commonly used
technique to assess drug cardiac liability. Cultured cardiomyocytes
are imaged by video microscopy and video edge detection techniques
are used to measure the rate at which the edge of a cell or cluster
of cells moves into and out of a user determined detection zone
(Gervais-Pingot et. al. 1994 Cell Biol Toxicol. 10(5-6):297-300;
Dolnikov et. al. 2006 Stem Cells. 4(2):236-45.). This method
requires dedicated equipment including specialised electronic
hardware to perform video rate edge detection, for example VED
motion edge detectors (www.crescent-electronics.com). Since the
method relies on detection of movement of an object edge with high
contrast the method is not suitable for all cultures, particularly
those with high cell density where the imaged area is full of
cells. Moreover since the technique requires an operator to
establish the region of analysis for edge detection for each sample
to be analysed the approach cannot be implemented in high
throughput.
[0005] US 2008/0304732 describes methods for evaluation of cellular
motion applied to cardiomyocyte cultures wherein time series images
are acquired and motion vectors are derived for successive
sequential pairs of images through the time series. These motion
vectors are based on the displacement of each individual cell
between consecutive image pairs and cellular motion is represented
by a series of displacement vector diagrams indicating the presence
and direction of movement between successive images. Cellular
motion data are then extracted using optical flow algorithms and
the resulting complex data reduced or decomposed using
factorisation methods, such as principal component analysis, to
allow motion data to be represented in low dimensional space, for
example as a waveform plot. These methods require significant
computer processing power and time to perform complex image and
data analysis.
[0006] The present invention seeks to overcome the limitations of
prior art methods by providing a system and a method of determining
motion of a biological object using simple image subtraction
techniques which are independent of cell density, image contrast or
the presence of detectable edges. Furthermore, the present
invention does not require complex data reduction or decomposition
techniques, and may be implemented using automated high-throughput
imaging equipment.
SUMMARY OF THE INVENTION
[0007] The method of the present invention provides a simple and
readily automated means of quantifying motion of a biological
object. The method is particularly suited for quantifying the
motion of cellular cultures such as measuring the frequency of
beating of cardiomyocytes.
[0008] According to a first aspect of the present invention, there
is provided a system (100) for automated determination of the
motion of a biological object, the system comprising: [0009] a) an
imager (104) for acquiring a first time series of images of the
object comprising successive frames (f.sub.1 to f.sub.n) and a
reference frame (f.sub.R); [0010] b) a processor (107) that is
operable to [0011] i) generate a time series of subtractive images
(f.sub.R-1 to f.sub.R-n); [0012] ii) derive one or more
measurements from the time series of subtractive images; and [0013]
iii) analyse said one or more measurements to quantify motion in
the first time series of images;
[0014] wherein generating the time series of subtractive images
(f.sub.R-1 to f.sub.R-n) comprises subtracting each successive
frame (f.sub.1 to f.sub.n) from said reference frame (f.sub.R); or
wherein
[0015] generating the time series of subtractive images (f.sub.R-1
to f.sub.R-n) comprises subtracting the reference frame (f.sub.R)
from each successive frame (f.sub.1 to f.sub.n).
[0016] In one aspect, each frame comprises a plurality of pixels
having an intensity value associated therewith and the processor is
operable to generate the time series of subtractive images by means
of the intensity values.
[0017] In another aspect, the one or more measurements is selected
from the group consisting of mean pixel intensity, total pixel
intensity and median pixel intensity.
[0018] In a further aspect, the object is a cultured organism or a
cell culture.
[0019] Preferably, the cultured organism is selected from the group
consisting of zebra fish or nematodes. Zebra fish (Danio rerio or
D. rerio) or nematodes (Caenorhabditis elegans or C. elegans) are
used in a variety of physiological, developmental, genetic, disease
and toxicological studies.
[0020] Preferably, the cell culture is selected from the group
consisting of bacterial cell culture, fungal cell culture, insect
cell culture, fish cell culture and mammalian cell culture. More
preferably, mammalian cell culture is a human cell culture. Most
preferably, the human cell culture is a cardiomyocyte cell
culture.
[0021] In a second aspect of the present invention, there is
provided a method of determining the motion of a biological object
comprising the steps of: [0022] i) acquiring a first time series of
images of said object comprising successive frames (f.sub.1 to
f.sub.n) and a reference frame (f.sub.R); [0023] ii) generating a
time series of subtractive images (f.sub.R-1 to f.sub.R-n); [0024]
iii) deriving one or more measurements from said time series of
subtractive images; and [0025] iv) analysing said one or more
measurements to quantify motion in said first time series of
images;
[0026] wherein, generating the time series of subtractive images
(f.sub.R-1 to f.sub.R-n) comprises subtracting each successive
frame (f.sub.1 to f.sub.n) from said reference frame (f.sub.R); or
wherein generating the time series of subtractive images (f.sub.R-1
to f.sub.R-n) comprises subtracting the reference frame (f.sub.R)
from each successive frame (f.sub.1 to f.sub.n).
[0027] In one aspect, each frame comprises a plurality of pixels
having an intensity value associated therewith and step ii)
involves either subtracting said intensity value of each said frame
from the corresponding intensity value of the reference frame
(f.sub.R) or subtracting the intensity value of the reference frame
(f.sub.R) from the corresponding intensity value of each frame.
[0028] In another aspect, the one or more measurements is selected
from the group consisting of mean pixel intensity, total pixel
intensity and median pixel intensity.
[0029] In a further aspect, the object is a cultured organism or a
cell culture.
[0030] Preferably, the cultured organism is selected from the group
consisting of zebra fish or C. elegans
[0031] Preferably, the cell culture is selected from the group
consisting of bacterial cell culture, fungal cell culture, insect
cell culture, fish cell culture and mammalian cell culture. More
preferably, the mammalian cell culture is a human cell culture.
Most preferably, the human cell culture is a cardiomyocyte cell
culture.
[0032] In a further aspect, the method is an automated method.
[0033] According to a third aspect of the present invention, there
is provided a use of the system or method as hereinbefore described
in drug discovery and/or toxicological testing. The system and/or
method can be used to evaluate the effect of a test agent, such as
a physical stimulus or chemical agent, in the motion of a
biological object. In particular, the system and/or method can be
used to determine whether a test agent has a deleterious or toxic
effect on the biological object. For example, the effect of the
test agent can be determined on the motion of cardiomyocyte cell
cultures by comparison with untreated control cultures, to
ascertain if the test agent has an adverse effect on the rate of
heart beat.
[0034] In a fourth aspect of the present invention, there is
provided a computer program product comprising machine instructions
operable to configure a data processing apparatus to implement the
method as hereinbefore described.
[0035] It will be apparent to one skilled in the art that the
method of the current invention for determining motion of a
biological object may be applied to many diverse applications
wherein motion is present. The motion may be fast, occurring over a
timescale of seconds or fractions of a second, including but not
limited to, cardiomyocyte contraction. Alternatively the motion may
be relatively slow, occurring over minutes, hours or days. Examples
of such motion include but are not limited to, movement of cells
during cell migration, motility, chemotaxis or wound healing or
movement or small organisms such as zebrafish and C. elegans. In
all cases motion is measured by acquiring a time series of images,
at an imaging frequency matched to the speed of motion under study,
and motion is quantified using a process of image subtraction as
described herein.
BRIEF DESCRIPTION OF THE DRAWINGS
[0036] In description of the method of the invention reference is
made to the following figures:
[0037] FIG. 1: Schematic diagram of principal features of the
present invention.
[0038] FIG. 2: Principle of pixel by pixel image subtraction.
[0039] FIG. 3: Principle of image subtraction of equivalent and
non-equivalent images.
[0040] FIG. 4: Image subtraction of video frame images of beating
cardiomyocytes.
[0041] FIG. 5: Schematic for image subtraction procedure to
determine motion in time series images.
[0042] FIG. 6: Subtractive images resulting from a time series of
video frames of beating cardiomyocytes produced by subtraction of a
series of video frame images from a reference video frame
image.
[0043] FIG. 7: Quantification of pixel intensity values in
subtractive images from a time series of video frames of beating
cardiomyocytes.
[0044] FIG. 8: Mean pixel values for subtractive images derived
from a time series of video frames of beating cardiomyocytes.
[0045] FIG. 9: Mean pixel values for an extended series of
subtractive images derived from a time series of video frames of
beating cardiomyocytes.
[0046] FIG. 10: Correlation analysis of mean pixel values derived
from an extended series of subtractive images derived from a time
series of video frames of beating cardiomyocytes.
[0047] FIG. 11: Mean pixel values for an extended series of
subtractive images derived from a time series of video frames of
beating cardiomyocytes.
[0048] FIG. 12: Fourier frequency analysis of data from an extended
series of subtractive images derived from a time series of video
frames of beating cardiomyocytes.
DETAILED DESCRIPTION
[0049] The principal aspects of the present invention are shown in
FIG. 1 illustrating a system [100] for automated determination of
the motion of a biological object, the object being exemplified by
a cell culture. Cells [102] cultured in one or more vessels [103]
are imaged by an imager [104] to produce a time series of images
starting with a first frame [105] and ending with a last frame
[106] where the number of images and the time interval between
images is chosen to be compatible with the motion to be analyzed.
Generally the time interval between frames is selected such that
the frequency of imaging is greater than the frequency or speed of
the cellular motion under analysis. The time series of images
[105-106] is processed using image subtraction [107] to produce a
series of subtractive images starting with a first image [108] and
ending with a final image [106] wherein each subtractive image
results from subtraction of successive images in the time series
from a reference image selected from the time series where the
reference image may be the first image in the series [105] or
alternatively may be the last image in the series [106]. This
procedure produces a derivative series of subtractive images
[108-109] where the maximum number of images is one less than the
number of images in the time series [105-106]. One or more
measurements are derived [110] from the time series of subtractive
images and these measurements subjected to analysis [111] to
quantify motion data [112].
[0050] Image subtraction is a readily implemented mathematical
means to determine differences between two images. To perform image
subtraction the pixel intensity values of equivalent pixels in the
two images are subtracted and the resulting absolute (ABS) value
(i.e. the difference in intensity values without a positive or
negative sign) is recorded. For 8 bit grey-scale (256 grey levels)
images where pixel intensity values may range from 0 (black) to 255
(white) the resulting pixel intensity values in subtractive images
may range from 0 (no difference at that pixel between images) to
255 (white pixel in one image and black pixel in second image).
[0051] To form a subtractive image the pixel intensity values (P)
of each equivalent pixel pair (i.e. pixels at the same x,y
coordinates in each image), P1xy and P2xy are subtracted and the
resulting values used to create a third derivative subtractive
image by applying the resultant pixel pair intensity difference
value to a pixel (.DELTA.Pxy) at an equivalent position (i.e. same
x,y coordinates) to generate a third image;
.DELTA.Pxy=ABS(P1xy-P2xy)
[0052] Alternatively other difference measures may be applied to
equivalent pixel pairs, for example;
.DELTA.Pxy= {square root over ((P1xy-P2xy).sup.2)}
[0053] Pixel by pixel image subtraction is illustrated
schematically in FIG. 2. A digital image [201] comprises a
3.times.3 array of pixels with (x,y) coordinates in the range 1-3
has a column of 3 mid-grey pixels [203]. A second digital image
[202] is identical to image [201] except that pixel (1,2) has been
replaced by a white pixel and pixel (2,2) has been replaced by a
mid-grey pixel [204]. In a time series of images the single pixel
difference between image [201] and [202] may represent movement of
an imaged object between the times of acquisition of the two
images. Subtraction of image [202] from image [201] yields a third
subtractive image [205] which contains information on the
differences between images [201] and [202]. Pixels which are
unchanged between the subtracted images, i.e. those pixels which
are white or mid-grey in both images [201] and [202] produce black
pixels in the subtractive image [205] since subtraction of equal
pixel values results in a subtractive image pixel value of 0.
Pixels which differ between images [201] and [202] produce pixel
values in the subtractive image [205] which have values equal to
the absolute value of the differences in pixel values. Consequently
both pixels (1,2) [206] and (2,2) [207] in the subtractive image
have mid-grey values representing the two pixels which differ
between image [201] and [202]. Analysis of pixel values in the
subtractive image [205] can therefore be used to provide a
quantitative measure of differences between the two images [201]
and [202].
[0054] The image subtraction process is illustrated schematically
for whole model images in FIG. 3 where subtraction of two identical
images, [A] and [A], produces a subtractive image [A-A] where all
pixel values are zero, yielding a pure black image. Subtraction of
two non-identical images [A] and [B] results in a subtractive image
[A-B] with a mix of black and mid-grey pixels. In the subtractive
image [A-B] black pixels indicate areas where images [A] and [B]
are identical, i.e. equivalent pixels are both white (255-255=0) or
are both mid-grey (128-128=0). Mid grey areas in the subtractive
image [A-B] indicate areas where images [A] and [B] differ, i.e.
pixels which are white in one image and mid-grey in the other image
since ABS(128-255)=127 or ABS(255-128)=127. Consequently if images
[A] and [B] are taken from a time series of images the subtractive
image [A-B] may be used to analyze motion and/or transformation,
e.g. the deforming of the circle in image [A] to an ellipse in
image [B].
[0055] In any time series of images, provided that the imaged
subject or specimen does not move relative to the camera or other
image capture device during imaging and further provided that
illumination intensity remains constant, any difference between
sequentially recorded images is due to motion within the subject
area and the time series of images may be used to analyse motion
within the subject using image subtraction by the method of the
present invention.
[0056] This process is illustrated in FIG. 4 using two frames from
a video-microscopy sequence of spontaneously beating cardiomyocytes
derived by differentiation of human embryonic stem cells.
Subtraction of two images from the time series [A] and [B] yields a
subtractive image [A-B] where the majority of the pixels have low
values (black and dark grey) indicating minimal differences between
the two images at these points. In other areas pixels have higher
values indicating differences between [A] and [B]. Since during the
acquisition of the video neither the cardiomyocyte culture nor the
video camera were moved, differences between frames [A] and [B]
which result in pixel values>0 in the subtractive image [A-B]
are due to motion within the imaged area, i.e. motion due to
cardiomyocyte beating.
[0057] The process of image subtraction may be extended to allow
the analysis of a number of images in a time series, for example
analysis of multiple frames through a video sequence. For analysis
of multiple frames in a time series of images (FIG. 5) individual
frames [f1] to [fn] are subtracted from a reference frame [fR] to
produce corresponding subtractive images [fR-1] to [fR-n]. The
reference frame [fR] may be selected from any point in the time
series outside of the series [f1] to [fn], i.e. [fR] may be
selected to be a frame preceding [f1] or subsequent to [fn], to
ensure that the resulting data abstracted from the series of
subtractive images [fR-1] to [fR-n] is contiguous. The process of
producing subtractive images can be readily implemented using
standard image processing software operations in a semi-automated
or fully automated process.
[0058] The series of subtractive images is then used to derive one
or more measures which may be used to quantify image differences
for graphical display and further analysis of motion within the
imaging time series. Suitable measures include, but are not limited
to, mean pixel intensity, total pixel intensity and median pixel
intensity. Plotting of the resulting data as a time series, e.g.
mean pixel intensity against image acquisition time, generates a
graphical depiction of the motion within the cell culture over
time. In the case of a culture exhibiting constant contraction and
relaxation motion, such as exhibited by spontaneously beating
cardiomyocyte cultures, the resulting graphical depiction of motion
in the time domain will take the form of a repeating wave.
[0059] To establish the frequency of detected motion, e.g. the beat
rate of a cardiomyocyte culture, data may be transformed from the
time domain to the frequency domain using standard Fourier Analysis
procedures (Fast Fourier Transforms, Walker, J. S. CRC Press.
1996). Alternatively correlation analysis may be performed on the
time series data to establish the repeat frequency.
EXAMPLE 1
[0060] Cardiomyocyes were obtained by differentiation of the H7
human embryonic stem cell (hESC) line as described in U.S. Pat. No.
7,452,718. Undifferentiated H7 hESC cells were seeded into 24 well
matrigel coated plates. After one week of growth as
undifferentiated cells, the medium was changed to RPMI+B27
supplement, with 50 ng/mL Activin A and 50 ng/mL BMP-4. After four
days, the growth factors were removed by medium exchange, and the
cells were then cultured for an additional 14 days in RPMI+B27
alone resulting in cells expressing Nkx2.5, .alpha.-actinin and
other markers of cardiac cells and which showed spontaneous
beating.
[0061] A video file of spontaneously beating cardiomyocytes derived
from hESC acquired at 10 frames/second was imported into Adobe
ImageReady and a representative series of 114 video frames
abstracted from the .wmv video file and exported to Abobe
Photoshop. The resulting file contained 114 layers each comprising
a single frame from the original video in ascending time series
order. Subtraction of pairs of images was achieved by designating
frame 114 as a reference frame and setting the Photoshop layer
blend mode to Difference for this layer. By sequentially selecting
each frame layer from 1-113 in turn a series of subtractive images
were obtained.
[0062] Examples of subtractive images are shown in FIG. 6 for
subtraction of image 10 from the reference frame [fR-10] through to
subtraction of frame 18 from the reference frame [fR-18]. Visual
inspection of the subtractive images [fR-10] to [fR-18] showed a
temporal variation in image pixel intensity from predominantly
black in [fR-10] through images with increasing and then decreasing
pixel intensities from [fR-11] to [fR-17] with the final image in
the series [fR-18] having predominantly low pixel intensity values.
These variations in pixel intensity in the subtractive images
indicate a cyclical variation in image difference between the image
frames and the reference frame.
[0063] Examination of pixel intensity values (FIG. 7) using the
Photoshop histogram display function showed that the variance in
pixel intensity observed by visual inspection correlated with
quantitative changes in pixel intensity with subtractive image
[fR-10] having a mean pixel intensity value of 6.42 and subtractive
image [fR-12] having a mean pixel intensity value of 25.21.
Plotting mean pixel intensity values for the series of subtractive
images (FIG. 8) confirmed the observed cyclical pattern of image
difference between respective frames and the reference frame
indicating that the method was detecting motion in the video times
series images.
[0064] To measure cardiomyocyte beating frequency in the video
images mean pixel intensities for 113 subtractive images were
plotted against their corresponding frame number (FIG. 9). The
results indicate continuous spontaneous beating with minor
variations in amplitude over the duration of the video sequence
with a frequency of 1 beat cycle every 8 frames. Cardiomyocyte beat
frequency was confirmed mathematically using correlation analysis
(FIG. 10) by calculating the sum of absolute differences between
the series of mean pixel intensity values over 113 frames (FIG. 8)
and the same data series shifted in one frame increments. A minimum
difference was obtained with an 8 frame shift in the data (FIG. 10)
consistent with an 8 frame cycle in the motion detected by image
subtraction. This was confirmed using linear regression analysis of
a staggered scatterplot of time series data using an 8 place shift
between x and y data, yielding a correlation of r.sup.2=0.99 and
slope=0.99. At the 10 frames/second used for video imaging this
equates to a beat rate of 1.25 beats/second or 75 beats/minute, a
typical value for spontaneously beating human cardiomyocytes.
EXAMPLE 2
[0065] Image frames from a second video file of spontaneously
beating cardiomyocytes derived from human embryonic stem cells
acquired at 10 frames/second were imported into Image J
(http://rsbweb.nih.gov/ij/) to give 332 time series images for
analysis. Four rectangular sub-regions were defined within the
imaged field and each sub-region in each of 331 time series images
was processed independently using the equivalent sub-region in
frame 332 as a reference for image subtraction. To measure
cardiomyocyte beating frequency in the different regions mean pixel
intensities for 331 subtractive images for each of the four regions
were plotted against their corresponding frame number (FIG. 11).
Fourier analysis performed in Microsoft Excel (FIG. 12) of data
from one of the regions showed a principal frequency of 2.07 Hz for
cardiomyocyte beating, equivalent to 124 beats/minute.
[0066] Whilst the present invention has been described in
connection with various embodiments, those skilled in the art will
be aware that many different embodiments and variations are
possible. All such variations and embodiments are intended to fall
within the scope of the present invention as defined by the
appended claims.
* * * * *
References